An article quoted in the November 2 issue of this newspaper (in a story headlined: “Problem Of Foodborne Illness Has Potentially Grown Worse; FSMA’s Promise Unfulfilled”), named: “Total Food Recall: Unsafe Foods Putting American Lives at Risk,” draws a somewhat doomsday scenario, concluding, “More needs to be done to protect Americans from the risk of unsafe food.” They assert:
“Important rules, standards, and inspections that could significantly improve food safety have been blocked, under-funded, or delayed, allowing the drumbeat of recalls to continue.”
Is the answer to the world’s problems more inspection, more regulation, more targets and, oddly, more studies, or better process? They go on...
“In other words, instead of things getting better, they appear to be getting worse,” “Our food safety practices are falling short.”
Before panic sets in, note the word “appear.” Are they getting worse or is reporting getting better? What exactly is the data that their conclusion is based on?
When people reach unreasonable conclusions, it is not to say they are not reasonable people. They are not alone, a number of articles over the years on dairy product safety, based on incomplete, and imperfectly presented data, draw equally destructive conclusions, IMHO.
The problem is they rely on the tools of enumerative, rather than analytic, or pragmatic statistics. A typical example of enumerative statistics is a political opinion poll; count and report. Drawing conclusions from enumerative data can be risky because it does not provide much in the way of context.
Analytic statistics is what Nate Silver used to so accurately predict the outcome of the last election, and is all about understanding data in context. Pragmatic statistics is what is used in process improvement, taking the theories generated by analytical statistics, and testing them, where opinion rubs up against reality.
Statistical data, studied in the abstract, is less science than abstract math. Logical connections can be found in the abstract that simply do not exist in the real world.
When looking at data presented in tables, in muddled sequence, arranged without real affinity, mixing areas of opportunity (a fancy way of saying comparing apples to oranges), it is easy to find things that don’t exist. Finding sense in nonsense is all too human. (I watch people do it with their financials all the time, until I can share with them what was shared with me: a new way of seeing data.)
Let me say, before we begin, that no sickness is the right amount. But before we jump whole hog into a bunch of new regulations and standards, doesn’t it make sense that we should take a look at what is really going on? Find the real cause, the root cause, the one that will really make things better?
The article continues, with a seemingly reasonable statement,
“The prominence of dairy in the study model reflects a relatively high number of reported outbreaks associated with raw milk compared with the quantity of raw milk consumed and issues related to Campylobacter spp. infection; these factors likely resulted in an overestimation of illnesses attributed to dairy.”
While true, it is hardly the only, nor the most important influence on the “overestimation” of illnesses attributed to dairy. Far more than pasteurized or unpasteurized, whether the food was commercially produced, has far more importance.1 Is it fair to lump the results from non-commercial and commercial sources together?
The other thing that kills understanding is relying on measures of comparison, rather than real numbers.
“An estimated 629 (43 percent) deaths each year were attributed to land animal, 363 to plant, and 94 to aquatic commodities… followed by dairy (10 percent) etc.”
Are they saying that every year 62.9 people die from dairy, or are they saying 10 percent of all illnesses? How many is that? Why give averages, posing as real numbers for everything, but dairy? Percents are meaningless unless you know the base number, and worse than meaningless if they are based on different numbers. As an example, a dollar compared to 10 dollars is 10 percent, to 100 dollars, only 1 percent, but it is still a dollar! How many deaths from dairy were there? The actual database is not that clear.
Averages presented as if they are real things, is a serious problem, especially when there are wide swings in variation point to point. If you average your pay and Bill Gates’, does that give you any idea about his salary or yours? Hardly! Averages are comparisons, not real things.
When articles throw a lot of numbers at you, without the hard data to back it up, or the context they sit in, how can you judge? Quantity is not quality.
And then comes this nonsense, parading as science: “One surprising fact consumers should take away from the CDC study of food borne illnesses between 1998 and 2008 is that dairy products, including milk, cheese, and ice cream, are big contributors to food borne illness,” commented Caroline Smith DeWaal, food safety director at the Center for Science in the Public Interest (CSPI).
“Dairy products ranked as the leading cause of hospitalizations linked to food borne illness; second to leafy greens in the numbers of illnesses; and second to poultry in the numbers of deaths,” Smith DeWaal noted.”
OH MY GOD!!! They are Scientists! We have to DO something!!!!! or do we? Data in tables and single datapoints give little to no context. Without an understanding, from the ground up, in an industry, someone from an interest group, can end up making what sounds like sense, but isn’t. The desire for hot button issues for publicity, and the lack of training in how to interpret data correctly, leads to conclusions that will make things worse, not better, and take the focus away from what could.
“Therefore, the incidence of reported outbreaks involving non pasteurized dairy products was ≈150× greater, per unit of dairy product consumed, than the incidence involving pasteurized products. If, as is probably more likely, <1% of dairy products are consumed non pasteurized, then the relative risk per unit of non pasteurized dairy product consumed would be even higher.” 2
Using other people’s interpretations of data, you get 150x more nonsense.
How do they define incidence: by outbreak? Number of people sickened? What is less than one percent? Is it .9 or .5 or .1 percent? With the population of the US hovering around 314 million people, that represents a spread of more than 2,800,000 people, out of which, how many consume dairy products?
It may seem like it, but I am not nitpicking here: Major changes are being called for, up to and including rules that could put an end to what real people depend on for their livelihood, aged raw milk cheese, based on nothing more than a throw of the dice. Abstract, mental trickery that will end up making things worse. Shooting from the hip misses the target, more times than not, and kills innocent bystanders.
Is a knee jerk reaction to less than stellar analysis based on imperfectly collected, organized, and presented data enough to put an end to one of the last bastions for small family farmers; while sending raw fluid milk production underground, where how it’s processes will never be improved.
The facts, seen in context, do not bear out these conclusions. I know, because I went back to the source, the CDC foodborne outbreak database.3 And I will share what I found with you, in an easier to understand format, next month.
Dan Strongin is managing partner and owner of Edible Solutions,
a consulting company focused on helping companies making great food
make a profit. He will be writing a monthly column in Cheese Reporter.
Strongin can be reached via phone at (510) 224-0493, or via e-mail at firstname.lastname@example.org. You can visit and blog with Dan at www.managenaturally.com.
Strongin Articles written for Cheese Reporter
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